Data Acquisition and Processing for GeoAI Models to Support Sustainable Agricultural Practices

There are growing opportunities to leverage new technologies and data sources to address global problems related to sustainability, climate change, and biodiversity loss. The emerging discipline of GeoAI resulting from the convergence of AI and Geospatial science (Geo-AI) is enabling the possibility to harness the increasingly available open Earth Observation data collected from different constellations of satellites and sensors with high spatial, spectral and temporal resolutions. However, transforming these raw data into high-quality datasets that could be used for training AI and specifically deep learning models are technically challenging. This paper describes the process and results of synthesizing labelled-datasets that could be used for training AI (specifically Convolutional Neural Networks) models for determining agricultural land use pattern to support decisions for sustainable farming. In our opinion, this work is a significant step forward in addressing the paucity of usable datasets for developing scalable GeoAI models for sustainable agriculture

Can linguistic features extracted from geo-referenced tweets help building function classification in remote sensing?

The fusion of two or more different data sources is a widely accepted technique in remote sensing while becoming increasingly important due to the availability of big Earth Observation satellite data. As a complementary source of geo-information to satellite data, massive text messages from social media form a temporally quasi-seamless, spatially multi-perspective stream, but with unknown and diverse quality. Despite the uncontrolled quality: can linguistic features extracted from geo-referenced tweets support remote sensing tasks? This work presents a straightforward decision fusion framework for very high-resolution remote sensing images and Twitter text messages. We apply our proposed fusion framework to a land-use classification task - the building function classification task - in which we classify building functions like commercial or residential based on linguistic features derived from tweets and remote sensing images. Using building tags from OpenStreetMap (OSM), we labeled tweets and very high-resolution (VHR) images from Google Maps. We collected English tweets from San Francisco, New York City, Los Angeles, and Washington D.C. and trained a stacked bi-directional LSTM neural network with these tweets. For the aerial images, we predicted building functions with state-of-the-art Convolutional Neural Network (CNN) architectures fine-tuned from ImageNet on the given task. After predicting each modality separately, we combined the prediction probabilities of both models building-wise at a decision level. We show that the proposed fusion framework can improve the classification results of the building type classification task. To the best of our knowledge, we are the first to use semantic contents of Twitter messages and fusing them with remote sensing images to classify building functions at a single building level